A way to move from SDE to Machine Learning

July 12, 2019

hi everyone today we are going to be talking about how to move from software engineering to machine learning in the past three months I have been reading up a lot about machine learning some of the findings have surprised me so I'll try to break this down into let's say five important tips the first thing is not to get lost in all the hype there's a lot of blog posts articles that I read and they are all about the consequences of machine learning as an engineer who wants to learn something we are not really too bothered about the consequences right now just just focus on getting the basic models working you know a person who can't write hello world can't really think about taking over factories the second point which i think is quite important is not to get lost in all the math behind these models these machine learning models and I know this is controversial and might sound you know completely wrong but I started from support vector machines went to quadratic programming which in turn is based on the simplex method it's a part of operations research when I started coding this it's going to have bugs because we are software engineers if you want to use a machine if you want to use a model you can use it also like an interface as in you given some inputs and get some outputs yeah you get some work done instead of understanding how it's working internally now if your purpose is academic or if your purpose is to go through a degree you have the time to spend understanding this model in depth but my personal experience is that when I try to learn machine learning this way I mean understanding all the mathematics it's like a rabbit hole I get lost at number three play to your strengths if your data engineer then consider this to be an extension of the problem that you have you know collecting data is something that you do really well inferring from it is something that you need to do now which is what machine learning entirely is but at least the parts where you collect data and clean it or filter it out that data is something that you have been dealing with for a really long time if you are an algorithm person then you can start thinking about all the approximation algorithms you have used possibly in programming contests possibly in college to understand how does a machine learning model behave or how do you actually evaluate its performance so if you if you play to your strengths and play to things that you already understand your mind is far more accepting of some new information instead of completely different information point number four is something that this channel will help you with it's finding reliable sources the sources that I use are either company block-posts let's say uber Google Facebook what these guys do is they write in terms of software engineering it makes a lot of sense again it's like playing To Your Strengths but also these companies they don't hype things as much as they possibly could the second thing is that because it's a practical case of machine learning there's a lot of decisions that people make before using a particular model instead of you know taking a toy problem and then deciding on how to solve it with different ways point number five which is probably the most important and although people know it they don't do it is coding you need to implement machine learning algorithms to understand it in depth unless you start coding on the terminal you won't be able to play around with that kind of model and of course you won't get the confidence of actually having programmed that machine learning code now there are a lot of courses online for machine learning a lot of them are quite good also this Udacity the skagle there's Coursera but there's one specific machine learning course created by educator for software engineers and once I found it I mean I didn't waste any time to collaborate with them and make sure that our community gets a discount for this machine learning for software engineers is really nice because it moves towards the coding bit initially and then you can get into the concepts so there's some results and outputs that you can see while you're making the transition to machine learning I actually like the way they focus on the pandas library before getting into the complex clustering and other algorithms the best thing is if you go through this channel and use the coupon code GS ml 20 then you get a 20% discount but this is a limited offer so the first 50 users are only going to get this and of course you can guess what GS stands for so those are the five tips I'd like to leave you with to transition from software engineering to machine learning if you have any doubts or suggestions you can leave them in the comments below of course you might have your own opinions you can leave that also in the comments below if you like the video then hit the like button and if you want notifications for further videos like this hit the subscribe button I'll see you next time

30 Comments

Great advice. There's only one part I don't agree with: the part where you talk about the need to understand the ML math. I think it's very important to understand that math. The problem is that, like you saw, most math material is either too vague or too detailed for someone who's starting ML. So I think it's best to do the steps you mentioned (start coding right away, using ML models right away, etc) and give yourself more time to learn the math. Take it slowly.

With a good learning plan and good study habits, you can learn in less than a year the math (linear algebra, calculus, and statistics) needed for ML. Once you do that, you'll feel so much more confident in your work.

For example, I'm sure you don't regret learning about SVMs. You just wish the material was better organized and better explained. 😉

Anyway, great video. I always learn something new from you. Keep up the great work.

Hello Gaurav, I have been watching your videos for the past 5 months and in each video, I get to learn a lot. So thank you very much for making informative videos. And there is one thing I want to ask, It's been 1 year since I graduated from University. I have done B.Tech in Computer Science Branch but I have less than 6 CGPA and I have been on a job hunt after graduation but haven't even got any yet. So, is there any chance for me to get a job as a software developer or should I go for other options? Please give me some advice.

hello nice artical. I have one question my background with technical skills is Node.js, React, Angular can is there any course available with this technology or to learn AI or machine we need python only.

How are you doing, I liked your video on machine learning as it is short and to the point.

I wanted ask you some questions on machine learning before that just giving you brief about me.

I am Ravindra Pawar working TCS from last 11 years in banking domain non technical. I have experience in cash management compliance, Trade finance, reconciliation and treasury also worked abroad for banking transition in commercial Banking and then my interest stared in machine learning.

I want to learn PEGA/PRPC certification i.e. CSA/CSSA and Blockchain. Hence wanted to ask you without having any technical background it ok to go head for this.

Hey Gaurav!! I am just a beginner in the coding and stuff and have just studied python programming and I am planning to learn machine for which I have sorted out some things , which may be or may not be right . It would be a great help if you would tell me what to do , as just about complete me first year as a engineer .So I have found these things which I should be studying for machine learning that is :1) Pandas 2) Mathplotlib3)Numpy 4) Scikit learn

And would-be doing this on anaconda (jupyter notebook ).I just heard up or just like a verification if I am going in the right direction, from people like you as you know at colleges they don't know much out of the course .So I hope I would find my answers .

At the starting you said learning maths behind algorithms is like going into a rabbit hole where one tends to get lost,it's an option best left to people pursuing academics. But towards the end (point no 5) you advise to implement machine learning algorithms…How can that be achieved without knowing maths behind the algorithms.

Hi Gaurav,Your video is really helpful for me.I'm a RPA developer with very limited knowledge of programming. I want to learn ML and leverage it's capability to solve the problems what I face my day to day life in automation.I am quite confused now. From where I should start. Can you please help me with your suggestions.? could you please guide me to the correct path. Where I should start? How should I progress?I'm willing to learn programming.

Hii i am akash i really want to learn Machine Learning but there is no one to guide me the right track to learn ML. can you guide me .as i am newer to this field so from i could start to reach to the best.